Subarachnoid Hemorrhage (SAH) detection is a critical, severe problem that confused clinical residents for a long time. With the rise of deep learning technologies, SAH detection made a significant breakthrough in recent ten years. Whereas, the performances are significantly degraded on imbalanced data, makes deep learning models have always suffered criticism. In this study, we present a DenseNet-LSTM network with Class-Balanced Loss and the transfer learning strategy to solve the SAH detection problem on an extremely imbalanced dataset. Compared to the previous works, the proposed framework not merely effectively integrate greyscale features the and spatial information from the consecutive CT scans, but also employ Class-Balanced loss and transfer learning to alleviate the adverse effects and broaden feature diversity respectively on an extreme SAH cases scarcity dataset, mimicking the actual situation of emergency departments. Comprehensive experiments are conducted on a dataset, consisted of 2,519 cases without hemorrhage cases and only 33 cases with SAH. Experimental results demonstrate the F-measure score of SAH detection achieved a remarkable improvement, the backbone DenseNet121 gained around 33% promotion after transfer learning, and on this basis, importing the Class-Balanced Loss and the LSTM structure, the F-measure score further increased 6.1% and 2.7% sequentially.
KEYWORDS: Visualization, Image classification, Visual analytics, Image filtering, Convolution, Data modeling, Solid modeling, Feature extraction, Medical research, RGB color model
Purpose of this paper is to present a method for visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endocytoscopic images. Endocytoscope enables us to perform direct observation of cells and their nuclei on the colon wall at maximum 500-times ultramagnification. For this new modality, computer-aided pathological diagnosis system is strongly required for the support of non-expert physicians. To develop a CAD system, we adopt convolutional neural network (CNN) as the classifier of endocytoscopic images. In addition to this classification function, based on CNN weights analysis, we develop a filter function that visualises decision-reasoning regions on classified images. This visualisation function helps novice endocytoscopists to develop their understanding of pathological pattern on endocytoscopic images for accurate endocytoscopic diagnosis. In numerical experiment, our CNN model achieved 90 % classification accuracy. Furthermore, experimental results show that decision-reasoning regions suggested by our filter function contain characteristic pit patterns in real endocytoscopic diagnosis.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.